How Data Reconciliation Can Save Healthcare Billions a Year

Are we getting better at data entry? 

Surprisingly, not really. At least when it comes to healthcare.

In a survey involving over 22,000 patients across three healthcare organizations, researchers found that more than 1 in 5 who accessed their electronic health records identified mistakes. 42% of these individuals considered the errors somewhat or very serious. 

A report published by Systematic Reviews found that fixing medication errors alone costs the global economy around $42 billion annually — a significant portion of those errors are transcription errors, like typos that alter a dosage.

So, why are we getting so much data wrong? Because there’s so much opportunity to get data wrong. Potentially tens of millions of medical records are processed every day in the United States. You’re bound to misspell someone’s name, misclick an option in a drop-down menu, or copy and paste the wrong text, eventually. 

Not to mention instances where people commit deliberate fraud. In June, the U.S. Department of Justice charged 193 defendants, including 76 medical professionals, with fraudulent billing, illegal prescription practices, and manipulation of medical records adding up to over $2.75 billion in losses.

It’s actually impressive there’s not more incorrect data. 

But how do we fix the incorrect data that does exist and prevent more of it.

What is data reconciliation?

Data reconciliation is the process of ensuring that data from different sources is accurate and consistent. In healthcare, this usually involves comparing data entries between different integrated platforms, incoming documents, and supporting documentation to identify and correct discrepancies.

Which all sounds great. The problem is it can be time-consuming and labor-intensive. In many cases, healthcare staff still have to manually cross-check records.

Automated tools and advanced technologies like DocKnow, our AI document processing platform, are beginning to offer alternative solutions. They can detect anomalies, flag inconsistent data entries, and even be programmed to recognize and reformat bad data. 

For example, if a patient ID contains a hyphen or special character on one document (e.g., “12345-AB”) but appears without it on another document (e.g., “12345AB”), DocKnow can recognize both as equivalent IDs and reformat one or both of them per your organization’s unique business rules. 

Or if a primary document lists a patient’s name as “Steven” but all supporting documents, including test requisition forms and discharge summaries, use “Stephen,” DocKnow can suggest to a human-in-the-loop reviewer that the former spelling might be incorrect before they submit it to another downstream system. 

The stakes are incredibly high.

A seemingly minor data error can lead to serious consequences, from billing issues and claim denials to life-threatening medical mistakes. By investing in technology like DocKnow, the industry can begin eliminating the risks of bad data and ensure more accurate, reliable patient care.

Data reconciliation is about building trust — in the data itself, the systems managing it, and the professionals who rely on accurate information to make critical care decisions. 

If you think DocKnow can help improve your enterprise’s data integrity, reach out to the Onymos team to get a live demo and learn more about what it can do.